Publikationen

Hepp, M.; Bachlechner, D. & Siorpaes, K. (2006): Harvesting Wiki Consensus - Using Wikipedia Entries as Ontology Elements. In: Proceedings of the First Workshop on Semantic Wikis - From Wiki to Semantics, co-located with the 3rd Annual European Semantic Web Conference (ESWC 2006), [Volltext] [Kurzfassung] [BibTeX][Endnote]

One major obstacle towards adding machine-readable annotation to

existing Web content is the lack of domain ontologies. While FOAF

and Dublin

Core are popular means for expressing relationships between Web resources

and between Web resources and literal values, we widely lack unique

identifiers

for common concepts and instances. Also, most available ontologies

have a

very weak community grounding in the sense that they are designed

by single

individuals or small groups of individuals, while the majority of

potential users

is not involved in the process of proposing new ontology elements

or achieving

consensus. This is in sharp contrast to natural language where the

evolution of

the vocabulary is under the control of the user community. At the

same time,

we can observe that, within Wiki communities, especially Wikipedia,

a large

number of users is able to create comprehensive domain representations

in the

sense of unique, machine-feasible, identifiers and concept definitions

which are

sufficient for humans to grasp the intension of the concepts. The

English

version of Wikipedia contains now more than one million entries and

thus the

same amount of URIs plus a human-readable description. While this

collection

is on the lower end of ontology expressiveness, it is likely the

largest living

ontology that is available today. In this paper, we (1) show that

standard Wiki

technology can be easily used as an ontology development environment

for

named classes, reducing entry barriers for the participation of users

in the

creation and maintenance of lightweight ontologies, (2) prove that

the URIs of

Wikipedia entries are surprisingly reliable identifiers for ontology

concepts, and

(3) demonstrate the applicability of our approach in a use case.

@inproceedings{Hepp:2006:HWC,
  author = {Hepp, Martin and Bachlechner, Daniel and Siorpaes, Katharina},
  title = {Harvesting Wiki Consensus - Using Wikipedia Entries as Ontology Elements},
  editor = {Völkel, Max and Schaffert, Sebastian},
  booktitle = {Proceedings of the First Workshop on Semantic Wikis -- From Wiki to Semantics, co-located with the 3rd Annual European Semantic Web Conference (ESWC 2006)},
  series = {Workshop on Semantic Wikis},
  publisher = {ESWC2006},
  year = {2006},
  url = {http://semwiki.org/semwiki2006},
  keywords = {2006 ezweb folksonomy ontology wikipedia},
  abstract = {One major obstacle towards adding machine-readable annotation to
	
	 existing Web content is the lack of domain ontologies. While FOAF
	and Dublin
	
	 Core are popular means for expressing relationships between Web resources
	
	 and between Web resources and literal values, we widely lack unique
	identifiers
	
	 for common concepts and instances. Also, most available ontologies
	have a
	
	 very weak community grounding in the sense that they are designed
	by single
	
	 individuals or small groups of individuals, while the majority of
	potential users
	
	 is not involved in the process of proposing new ontology elements
	or achieving
	
	 consensus. This is in sharp contrast to natural language where the
	evolution of
	
	 the vocabulary is under the control of the user community. At the
	same time,
	
	 we can observe that, within Wiki communities, especially Wikipedia,
	a large
	
	 number of users is able to create comprehensive domain representations
	in the
	
	 sense of unique, machine-feasible, identifiers and concept definitions
	which are
	
	 sufficient for humans to grasp the intension of the concepts. The
	English
	
	 version of Wikipedia contains now more than one million entries and
	thus the
	
	 same amount of URIs plus a human-readable description. While this
	collection
	
	 is on the lower end of ontology expressiveness, it is likely the
	largest living
	
	 ontology that is available today. In this paper, we (1) show that
	standard Wiki
	
	 technology can be easily used as an ontology development environment
	for
	
	 named classes, reducing entry barriers for the participation of users
	in the
	
	 creation and maintenance of lightweight ontologies, (2) prove that
	the URIs of
	
	 Wikipedia entries are surprisingly reliable identifiers for ontology
	concepts, and
	
	 (3) demonstrate the applicability of our approach in a use case.}
  }

%0 = inproceedings
%A = Hepp, Martin and Bachlechner, Daniel and Siorpaes, Katharina
%B = Proceedings of the First Workshop on Semantic Wikis -- From Wiki to Semantics, co-located with the 3rd Annual European Semantic Web Conference (ESWC 2006)
%D = 2006
%I = ESWC2006
%T = Harvesting Wiki Consensus - Using Wikipedia Entries as Ontology Elements
%U = http://semwiki.org/semwiki2006

Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G. (2006): Mining Association Rules in Folksonomies. In: Data Science and Classification. Proceedings of the 10th IFCS Conf., Heidelberg. [Volltext] [Kurzfassung] [BibTeX][Endnote]

Social bookmark tools are rapidly emerging on the Web. In such

systems users are setting up lightweight conceptual structures

called folksonomies. These systems provide currently relatively few

structure. We discuss in this paper, how association rule mining

can be adopted to analyze and structure folksonomies, and how the results can be used

for ontology learning and supporting emergent semantics. We

demonstrate our approach on a large scale dataset stemming from an

online system.

@inproceedings{Schmitz_et_al_2006,
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  title = {Mining Association Rules in Folksonomies},
  editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and Žiberna, A.},
  booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.},
  series = {Studies in Classification, Data Analysis, and Knowledge Organization},
  publisher = {Springer},
  address = {Heidelberg},
  year = {2006},
  pages = {261--270},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf},
  keywords = {2006 ezweb folksonomy ontology},
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. These systems provide currently relatively few
structure. We discuss in this paper, how association rule mining
can be adopted to analyze and structure folksonomies, and how the results can be used
for ontology learning and supporting emergent semantics. We
demonstrate our approach on a large scale dataset stemming from an
online system.}
  }

%0 = inproceedings
%A = Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd
%B = Data Science and Classification. Proceedings of the 10th IFCS Conf.
%C = Heidelberg
%D = 2006
%I = Springer
%T = Mining Association Rules in Folksonomies
%U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf